In this project we developed a new simulation-based technique to reliably predict the annual electricity yield from an arbitrarily oriented and obstructed photovoltaic
(PV) array located anywhere on the planet. The technique considers detailed surrounding geometry such as trees and buildings, hourly direct and diffuse solar
radiation data as well as instantaneous solar cell efficiencies due to varying roof temperatures. Required simulation inputs are standard local weather station data
as well as LIDAR point clouds.

As a proof of concept, the we teamed up with the City of Cambridge and Eduardo Berlin from
Modern Development Studio and generated
an interactive Solar Map of Cambridge. MIT created an annual electricity yield map from PV
for all Cambridge rooftops. The data shows which roofs in Cambridge have excellent, good, poor and no solar potential for PV. The map has a resolution of 5' by 5'.
Modern Develoment Studio devleopped the online viewer and a financial analysis module that considers federal and state incentives for residential and commercial PV
installtions as well as estimated payback times for PV systems with excellent or good solar potential. The City of Cambridge provided the underlying LiDAR data and
Google ortho-images.
The basic steps of the simulation technique are shown below. More details can be found here.

1 - Arial photo of the MIT campus with the Kresge Auditorium.

2 - The LIDAR data from the City of Cambridge consists of 126 million data points.

3 - A simplification algorithm was used to reduce the data set to 9 million points.

4 - Using Delauny triangulation and GIS floor print data a 3D model of Cambridge with different surface properties was generated.

7 - Electriciy yields were classified and mapped on top of Google sattelite images.

8 - The financial analysis considers public incentives to calculate the armortisation time for excellent and good roof areas.

MIT's portion of the project has been supported by the National Science Foundation under Grant No. 1038264. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation.